An intelligent system for forest fire risk prediction and fire fighting management in Galicia

Alonso‐Betanzos, Amparo; Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha; Hernández-Pereira, Elena; Andrade, M. I. Paz; Jiménez, E.; Soto, José Luis Legido; Carballas, T.

doi:10.1016/s0957-4174(03)00095-2

Cited by 112 publications

(65 citation statements)

References 4 publications

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“…Accordingly, the temporal dimensions for fire observations may be daily (Crosby 1954;Haines et al 1983;Alonso-Betanzos et al 2003;Lozano et al 2007;Albertson et al 2009;Wotton et al 2010;Padilla and Vega-Garcia 2011;Sakr et al 2011), monthly (Preisler et al 2004;Boulanger et al 2014) or yearly (Todd and Kourtz 1991;Prestemon and Butry 2005;Hu and Zhou 2014;Karouni et al 2014). However, longer time-spans of several years are the most frequent (Pew and Larsen 2001;Chuvieco et al 2008;Avila-Flores et al 2010;GonzalezOlabarria et al 2011;West et al 2016).…”

Section: Temporal Span For Modellingmentioning

confidence: 99%

“…Temporal variables drive short-term models. High mean and maximum temperatures (Alonso-Betanzos et al 2003;Preisler et al 2004;Carvalho et al 2008Carvalho et al , 2010Vilar et al 2010;Magnussen and Taylor 2012;Bedia et al 2014;Karouni et al 2014;Najafabadi et al 2015), low precipitation (Albertson et al 2009;Vasilakos et al 2009;Zhang et al 2010;Plucinski et al 2014;Guo et al 2016a) and low relative humidity (AlonsoBetanzos et al 2003;Padilla and Vega-Garcia 2011;Chang et al 2013;Karouni et al 2014) favour fires and are often used in models. However, fire science has developed methods to estimate the decrease in moisture content caused by weather on litter and fine fuels, medium compact organic layers and deep organic soil layers or heavy fuels for fire danger rating (Dimitrakopoulos et al 2011).…”

Section: Predictors For Short-term Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Human-caused fire occurrence modelling in perspective: a review

Costafreda-Aumedes

Comas

Vega‐García

2017

Int. J. Wildland Fire

132

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Abstract. The increasing global concern about wildfires, mostly caused by people, has triggered the development of human-caused fire occurrence models in many countries. The premise is that better knowledge of the underlying factors is critical for many fire management purposes, such as operational decision-making in suppression and strategic prevention planning, or guidance on forest and land-use policies. However, the explanatory and predictive capacity of fire occurrence models is not yet widely applied to the management of forests, fires or emergencies. In this article, we analyse the developments in the field of human-caused fire occurrence modelling with the aim of identifying the most appropriate variables and methods for applications in forest and fire management and civil protection. We stratify our worldwide analysis by temporal dimension (short-term and long-term) and by model output (numeric or binary), and discuss management applications. An attempt to perform a meta-analysis based on published models proved limited because of non-equivalence of the metrics and units of the estimators and outcomes across studies, the diversity of models and the lack of information in published works.

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Section: Temporal Span For Modellingmentioning

confidence: 99%

Section: Predictors For Short-term Studiesmentioning

confidence: 99%

Human-caused fire occurrence modelling in perspective: a review

Costafreda-Aumedes

Comas

Vega‐García

2017

Int. J. Wildland Fire

132

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“…Galicia and Asturias are characterized by year-round mild temperatures, with maximum values of 12 °C in winter and minimum values of 15 °C in summer [28]. Agencies fighting forest fires work on the basis of a grid that divides Spain into 10 × 10 km cells, based on the UTM (Universal Transverse Mercator) projection [7]. The historical database on forest fires in Spain is based on this grid.…”

Section: Study Area and Datasetmentioning

confidence: 99%

“…Different models provide fire danger indices based on the combination of variables such as weather, fire history, proximity to roads and/or people, etc. [6][7][8]. However, field measurements are sometimes required, which are very costly in economic terms, time and human resources.…”

Section: Introductionmentioning

confidence: 99%

Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS Images

2014

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Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation and moisture indices can be used to monitor vegetation status; however, the different indices may perform differently depending on the vegetation species. Eight different spectral indices were selected to determine the most appropriate index in Galicia. This study was extended to the adjacent region of Asturias. Six years of MODIS (Moderate Resolution Imaging Spectroradiometer) images, together with ground fire data in a 10 × 10 km grid basis were used. The percentage of fire events met the variations suffered by some of the spectral indices, following a linear regression in both Galicia and Asturias. The Enhanced Vegetation Index (EVI) was the index leading to the best results. Based on these results, a simple fire danger model was established, using logistic regression, by combining the EVI variation with other variables, such as fire history in each cell and period of the year. A seventy percent overall concordance was obtained between estimated and observed fire frequency.

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“…Tailor-made wildfire decision support systems have been developed and used in a number of different wildfire regions, including Spain (Alonso-Betanzos et al, 2003), France (Figueras Jové et al, 2014), Italy (Losso et al, 2012), Turkey (Gumusay and Sahin, 2009), the US (Noonan-Wright et al, 2011), and in the alpine areas of Europe (Corgnati et al, 2008). Despite the large number of systems, few of them can be considered fully integrated solutions to the wildfire management problem, and generally each of the aforementioned systems has emphasized a specific functionality.…”

Section: Introductionmentioning

confidence: 99%

AEGIS: a wildfire prevention and management information system

Kalabokidis

Ager

Finney

et al. 2016

Nat. Hazards Earth Syst. Sci.

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Abstract. We describe a Web-GIS wildfire prevention and management platform (AEGIS) developed as an integrated and easy-to-use decision support tool to manage wildland fire hazards in Greece (http://aegis.aegean.gr). The AEGIS platform assists with early fire warning, fire planning, fire control and coordination of firefighting forces by providing online access to information that is essential for wildfire management. The system uses a number of spatial and non-spatial data sources to support key system functionalities. Land use/land cover maps were produced by combining field inventory data with high-resolution multispectral satellite images (RapidEye). These data support wildfire simulation tools that allow the users to examine potential fire behavior and hazard with the Minimum Travel Time fire spread algorithm. End-users provide a minimum number of inputs such as fire duration, ignition point and weather information to conduct a fire simulation. AEGIS offers three types of simulations, i.e., single-fire propagation, point-scale calculation of potential fire behavior, and burn probability analysis, similar to the FlamMap fire behavior modeling software. Artificial neural networks (ANNs) were utilized for wildfire ignition risk assessment based on various parameters, training methods, activation functions, pre-processing methods and network structures. The combination of ANNs and expected burned area maps are used to generate integrated output map of fire hazard prediction. The system also incorporates weather information obtained from remote automatic weather stations and weather forecast maps. The system and associated computation algorithms leverage parallel processing techniques (i.e., High Performance Computing and Cloud Computing) that ensure computational power required for real-time application. All AEGIS functionalities are accessible to authorized end-users through a web-based graphical user interface. An innovative smartphone application, AEGIS App, also provides mobile access to the webbased version of the system.

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An intelligent system for forest fire risk prediction and fire fighting management in Galicia

Cited by 112 publications

References 4 publications

Human-caused fire occurrence modelling in perspective: a review

Human-caused fire occurrence modelling in perspective: a review

Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS Images

AEGIS: a wildfire prevention and management information system

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